TOPC Cohort 3 Analysis

Pre-test

Graphs for pre-test survey analysis for city governments.

Necessary packages

Code

# Set a CRAN mirror
options(repos = c(CRAN = "https://cloud.r-project.org"))

# Install necessary packages
# install.packages("magrittr")
# install.packages("dplyr")
# install.packages("ggplot2")
# install.packages("knitr")
# install.packages("kableExtra")
# install.packages("survey")
# install.packages("plotly")

# Load the packages
library(magrittr)
library(dplyr)
library(plotly)
library(ggplot2)
library(knitr)
library(kableExtra)
library(survey)

Survey design weights

Code

# Read the data from the CSV file
cohort3BackgroundGov <- read.csv("~/Documents/R Projects/TOPC-impact-evaluation-dashboard/Cohort 3/cohort3BackgroundGov3.csv")

# Define population and sample sizes
population_counts <- c(Akron = 8, Detroit = 9, Macon = 10, Miami = 13)
sample_counts <- c(Akron = 7, Detroit = 1, Macon = 4, Miami = 3)

# Total population and sample size
total_population <- sum(population_counts)
total_sample <- sum(sample_counts)

# Calculate the weights
weights <- (population_counts / total_population) / (sample_counts / total_sample)

# Print the calculated weights
print(weights)

    Akron   Detroit     Macon     Miami 
0.4285714 3.3750000 0.9375000 1.6250000 

Code

# Set the option for handling a single PSU in a stratum as certainty
options(survey.lonely.psu="certainty")

# Map between city names 
city_map <- list(
  "Miami-Dade County, FL" = "Miami",
  "Akron, OH" = "Akron",
  "Detroit, MI" = "Detroit",
  "Macon-Bibb County, GA" = "Macon"
)

# Update dataframe to use the mapped names
cohort3BackgroundGov$city_mapped <- unlist(lapply(cohort3BackgroundGov$q4, function(x) city_map[[x]]))

# Assign weights using the mapped city names
cohort3BackgroundGov$weights <- weights[cohort3BackgroundGov$city_mapped]

# Define the survey design
design <- svydesign(ids = ~1, 
                    strata = ~city_mapped, 
                    weights = ~weights, 
                    data = cohort3BackgroundGov)

Descriptive Data Analysis

Code

# # Function to calculate and plot weighted frequencies for a single question
# analyze_likert_question <- function(question, design) {
#   formula <- as.formula(paste("~", question))
#   likert_distribution <- svytable(formula, design)
#   likert_df <- as.data.frame(likert_distribution)
#   names(likert_df) <- c("Response", "Frequency")
# 
#   ggplot(likert_df, aes(x = Response, y = Frequency)) +
#     geom_bar(stat = "identity") +
#     labs(title = paste("Distribution of Responses for", question))
# }
# 
# questions_to_analyze <- c("q8", "q9", "q10", "q11", "q13", "q14", "q15", "q16", "q17", "q18", "q19", "q21", "q22", "q23")
# plots <- lapply(questions_to_analyze, function(q) analyze_likert_question(q, design))
# 
# # You can now view the plots for each question
# plots[[1]] # For example, to view the plot for the first question in the list

Per Question

For Akron, Macon and Miami Strata: We used the survey package functions like svyquantile and sqrt(svyvar) for calculating the median and standard deviation for strata other than Detroit. So, these statistics do take into account the sampling weights. The survey package is designed to incorporate sampling weights into its calculations, which means that the median and standard deviation for Akron (as well as for Macon and Miami) reflect the weighted characteristics of your sample.

For the Detroit Stratum: For the Detroit stratum, we calculated the median and standard deviation directly because of the single PSU issue. This direct calculation does not take into account the sampling weights since it bypasses the survey package. In cases where there’s only one PSU, the concept of weighting becomes less relevant, as there’s no variance within the stratum to adjust for. The summary statistics for Detroit are based solely on the available data for that stratum.

So, in summary:

• For Akron, Macon, and Miami, the summary statistics listed are weighted and take into account the sampling design.

• For Detroit, the statistics are unweighted due to the direct calculation method used.

This approach is consistent with handling complex survey data where some strata have limitations like having only one PSU. This method of handling of the data aligns well with these challenges.

Code

# Calculate central tendancy per question per stratum
# List of questions to analyze
questions_to_analyze <- c("q8", "q9", "q10", "q11", "q13", "q14", "q15", "q16", "q17", "q18", "q19", "q21", "q22", "q23")

# Define the strata
strata <- c("Akron", "Detroit", "Macon", "Miami")

# Initialize a list to store stats for each question and stratum
all_strata_stats <- list()

# Loop through each stratum and question to calculate stats
for (stratum in strata) {
    stratum_data <- subset(cohort3BackgroundGov, city_mapped == stratum)
    stratum_stats <- list()
    for (question in questions_to_analyze) {
        if (stratum != "Detroit") {
            # Use survey package functions for strata other than Detroit
            median_val <- svyquantile(~get(question), subset(design, city_mapped == stratum), 0.5, na.rm = TRUE)
            sd_val <- sqrt(svyvar(~get(question), subset(design, city_mapped == stratum), na.rm = TRUE))
        } else {
            # Direct calculation for Detroit
            question_data <- stratum_data[[question]]
            if (!all(is.na(question_data))) {
                median_val <- median(question_data, na.rm = TRUE)
                sd_val <- sd(question_data, na.rm = TRUE)
            } else {
                median_val <- NA
                sd_val <- NA
            }
        }
        stratum_stats[[question]] <- list(median = median_val, sd = sd_val)
    }
    all_strata_stats[[stratum]] <- stratum_stats
}

# # Print stats for each question and stratum
# for (stratum in strata) {
#     cat("Statistics for stratum:", stratum, "\n")
#     for (question in questions_to_analyze) {
#         cat("Question", question, ":\n")
#         print(all_strata_stats[[stratum]][[question]])
#         cat("\n")
#     }
# }

# Initialize an empty data frame for the results
results_df <- data.frame(Stratum = character(),
                         Question = character(),
                         Median = numeric(),
                         StandardDeviation = numeric(),
                         stringsAsFactors = FALSE)

# Loop through each stratum and question to collect stats
counter <- 1
for (stratum in strata) {
    for (question in questions_to_analyze) {
        # Extract median and standard deviation values
        median_val <- all_strata_stats[[stratum]][[question]]$median
        sd_val <- all_strata_stats[[stratum]][[question]]$sd

        # Format the values correctly
        median_val <- ifelse(is.list(median_val), median_val[[1]], median_val)
        sd_val <- ifelse(is.list(sd_val), sd_val[[1]], sd_val)

        # Ensure that NA values are handled properly
        median_val <- ifelse(is.null(median_val) || length(median_val) == 0, NA, median_val)
        sd_val <- ifelse(is.null(sd_val) || length(sd_val) == 0, NA, sd_val)

        # Append the row to the data frame
        results_df[counter, ] <- c(Stratum = stratum,
                                   Question = question,
                                   Median = median_val,
                                   StandardDeviation = sd_val)
        counter <- counter + 1
    }
}

# Create the table using kable and kableExtra
results_table <- kable(results_df, format = "html", 
                       col.names = c("Stratum", "Question", "Median", "Standard Deviation")) %>%
                 kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

# Print the table
results_table

Stratum	Question	Median	Standard Deviation
Akron	q8	2	1.38013111868471
Akron	q9	2	1.38013111868471
Akron	q10	1	0.377964473009227
Akron	q11	3	1.38013111868471
Akron	q13	3	0.755928946018454
Akron	q14	2	0.951189731211342
Akron	q15	1	0.786795792469443
Akron	q16	3	1.52752523165195
Akron	q17	2	0.899735410842437
Akron	q18	1	0.755928946018454
Akron	q19	3	1.2724180205607
Akron	q21	3	1.4638501094228
Akron	q22	4	1.13389341902768
Akron	q23	2	1.0690449676497
Detroit	q8	3	NA
Detroit	q9	3	NA
Detroit	q10	2	NA
Detroit	q11	5	NA
Detroit	q13	5	NA
Detroit	q14	5	NA
Detroit	q15	2	NA
Detroit	q16	5	NA
Detroit	q17	4	NA
Detroit	q18	2	NA
Detroit	q19	4	NA
Detroit	q21	5	NA
Detroit	q22	4	NA
Detroit	q23	2	NA
Macon	q8	3	0.957427107756338
Macon	q9	3	0.577350269189626
Macon	q10	2	1.29099444873581
Macon	q11	4	0.816496580927726
Macon	q13	3	0.957427107756338
Macon	q14	3	1.29099444873581
Macon	q15	3	0.577350269189626
Macon	q16	4	0.5
Macon	q17	3	0.577350269189626
Macon	q18	2	1.5
Macon	q19	4	0.816496580927726
Macon	q21	3	0.957427107756338
Macon	q22	4	0.816496580927726
Macon	q23	4	0
Miami	q8	4	1
Miami	q9	4	0.577350269189626
Miami	q10	4	2.08166599946613
Miami	q11	3	2
Miami	q13	4	2.08166599946613
Miami	q14	4	1.73205080756888
Miami	q15	4	0.577350269189626
Miami	q16	4	0.577350269189626
Miami	q17	4	0.577350269189626
Miami	q18	5	0.577350269189626
Miami	q19	2	2.08166599946613
Miami	q21	2	2.08166599946613
Miami	q22	4	1.52752523165195
Miami	q23	3	1.15470053837925

Per City Government Team

The below table shows summary statistics for respondents from each city and county government team who rated their skills in community research, product stewardship, and partnerships using a likert scale of 5 - Expertise in concept and terminology to 1 - New to concept and terminology.

Detroit and Miami show higher self-assessed skill levels (median of 4), but Miami has more variability in responses, meaning there’s more diversity in how respondents rate their skills, ranging from moderate to high levels of familiarity. Akron has the lowest self-assessed skill level (median of 2) with less variability, suggesting a general consensus around a basic level of understanding, while Macon is in the middle (median of 3) with moderate variability. For Macon, there is a wider range of self-assessed skill levels, though still relatively clustered around the median.

• 1 - New to concept and terminology: The respondent has no previous knowledge or experience with the skill in question. They are entirely unfamiliar with the concepts or terminology.

• 2 - The respondent has very basic knowledge or understanding of the skill. They may recognize it but are not comfortable with it.

• 3 - The respondent has a moderate level of familiarity with the skill. They have some experience or understanding but may not feel fully proficient.

• 4 - The respondent is quite familiar and comfortable with the skill, although not at the highest level of proficiency.

• 5 - Expertise in concept and terminology: The respondent has a very high level of familiarity with the skill. They have comprehensive knowledge and experience, and they understand the concepts and terminology thoroughly.

Code

# Central tendancy per question per stratum
strata <- c("Akron", "Detroit", "Macon", "Miami")

# Check if 'all_strata_stats' is a list with an entry for each stratum
# print(strata)
# print(names(all_strata_stats))

calculate_overall_stats <- function(stratum_stats) {
    combined_medians <- unlist(lapply(stratum_stats, function(x) x$median), use.names = FALSE)
    combined_sds <- unlist(lapply(stratum_stats, function(x) x$sd), use.names = FALSE)
    
    overall_median <- median(combined_medians, na.rm = TRUE)
    overall_sd <- sd(combined_sds, na.rm = TRUE)
    
    return(list(median = overall_median, sd = overall_sd))
}

overall_stratum_stats <- list()

for (stratum in strata) {
    if (stratum %in% names(all_strata_stats)) {
        overall_stratum_stats[[stratum]] <- calculate_overall_stats(all_strata_stats[[stratum]])
    } else {
        overall_stratum_stats[[stratum]] <- list(median = NA, sd = NA)
    }
}

# Print overall stats for each stratum
# for (stratum in strata) {
#     cat("Overall statistics for stratum:", stratum, "\n")
#     print(overall_stratum_stats[[stratum]])
#     cat("\n")
# }

overall_stats_df <- data.frame(Stratum = names(overall_stratum_stats),
                               Median = sapply(overall_stratum_stats, function(x) x$median),
                               StandardDeviation = sapply(overall_stratum_stats, function(x) x$sd),
                               stringsAsFactors = FALSE)

overall_stats_table <- kable(overall_stats_df, format = "simple",
                             col.names = c("Stratum", "Overall Median", "Overall Standard Deviation"))

print(overall_stats_table)

          Stratum    Overall Median   Overall Standard Deviation
--------  --------  ---------------  ---------------------------
Akron     Akron                   2                    0.3392122
Detroit   Detroit                 4                           NA
Macon     Macon                   3                    0.3835532
Miami     Miami                   4                    0.6726299

Participant Background

The survey had a total of respondents from:

• Akron = 7

• Detroit = 1

• Macon = 4

• Miami = 3

However, we applied weights in this survey analysis to adjust the results to compensate for different probabilities of selection, non-response, and to adjust the sample to known population totals. The weights we provided are the factor by which each city’s responses are to be multiplied to get a representation that is more reflective of the population and the quota sampling design of this study.

For example, to interpret the graph with weight counts for Akron, OH, the city has an original count of 7 respondents and a weight of 0.4285714. The weighted count would be 7 * 0.4285714. This would suggest that the weighted frequency count for Akron should be about 3 after rounding, which matches the bar graph.

Q7. Which city/county are you representing? [Format - City/County, State abbreviation] (domestic)]

Code

# Single-variable frequency plot
q4_frequency <- svytable(~q4, design = design)

q4_pre_frequency_plot <- ggplot(as.data.frame(q4_frequency), aes(x = q4, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  labs(title = "Q7. Which city/county are you representing?",
       subtitle = paste("Sample size =", total_sample),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q4_pre_frequency_plot)

Code

ggsave("plots/q4_pre_frequency_plot.png", q4_pre_frequency_plot)

Q6. Please describe your racial/ethnic identity. Select all that apply.

Code

# Single-variable frequency plot
q3_frequency <- svytable(~q3, design = design)

q3_pre_frequency_plot <- ggplot(as.data.frame(q3_frequency), aes(x = q3, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  labs(title = "Q8. Which title best describes your role and level 
of seniority at work?",
       subtitle = paste("Sample size =", total_sample),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q3_pre_frequency_plot)

Code

ggsave("plots/q3_pre_frequency_plot.png", q3_pre_frequency_plot)

Q8. Which title best describes your role and level of seniority at work?

Code

# Single-variable frequency plot
q5_frequency <- svytable(~q5, design = design)

q5_pre_frequency_plot <- ggplot(as.data.frame(q5_frequency), aes(x = q5, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  labs(title = "Q8. Which title best describes your role and level 
of seniority at work?",
       subtitle = paste("Sample size =", total_sample),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q5_pre_frequency_plot)

Code

ggsave("plots/q5_pre_frequency_plot.png", q5_pre_frequency_plot)

Q10. How many years have you worked for your city or county?

Code

# Single-variable frequency plot
q7_frequency <- svytable(~q7, design = design)

q7_pre_frequency_plot <- ggplot(as.data.frame(q7_frequency), aes(x = q7, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  labs(title = "Q10. How many years have you worked for your city or county?",
       subtitle = paste("Sample size =", total_sample),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q7_pre_frequency_plot)

Code

ggsave("plots/q7_pre_frequency_plot.png", q7_pre_frequency_plot)

Goal 2: Participant Confidence and Competence

Community Research

Please rate your familiarity with the following skills.

Q11. Conducting community research (leading interviews, surveys, observations, etc.)

More than half respondents agree they learned new skills in community research practices

Akron is newest to community research skills, concepts and terminologies, with Miami and Macon most experienced.

Code

# Single-variable frequency plot
q8_frequency <- svytable(~q8, design = design)

q8_pre_frequency_plot <- ggplot(as.data.frame(q8_frequency), aes(x = q8, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "More than half respondents agree they learned new skills in community 
research practices.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q8_pre_frequency_plot)

Code

ggsave("plots/q8_pre_frequency_plot.png", q8_pre_frequency_plot)

Code

# Cross-tabulation with cities
q8_cross_tab_city <- svytable(~q8 + city_mapped, design = design)

q8_levels_ordered <- as.factor(as.data.frame(q8_cross_tab_city)$q8)
q8_levels_ordered <- factor(q8_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q8_cross_tab_city_df <- as.data.frame(q8_cross_tab_city)
q8_cross_tab_city_df$q8 <- factor(q8_cross_tab_city_df$q8, levels = c("1", "2", "3", "4", "5"))

# Generate the plot
q8_pre_cross_tab_city_plot <- ggplot(q8_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q8)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F")) +
  labs(title = "Q11. Conducting community research (leading interviews, 
surveys, observations, etc.).",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

# Print the plot to display it in the document
print(q8_pre_cross_tab_city_plot)

Code

ggsave("plots/q8_pre_cross_tab_city_plot.png", q8_pre_cross_tab_city_plot)

Q12. Testing ideas or prototypes with community members

Code

# Single-variable frequency plot
q9_frequency <- svytable(~q9, design = design)

all_levels_df <- data.frame(q9 = factor(1:5, levels = 1:5), Freq = integer(5))
q9_frequency_df <- merge(all_levels_df, as.data.frame(q9_frequency), by = "q9", all.x = TRUE)
q9_frequency_df$Freq <- rowSums(q9_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q9_pre_frequency_plot <- ggplot(q9_frequency_df, aes(x = q9, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q12. Testing ideas or prototypes with community members.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q9_pre_frequency_plot)

Code

ggsave("plots/q9_pre_frequency_plot.png", q9_pre_frequency_plot)

Code

# Cross-tabulation with cities
q9_cross_tab_city <- svytable(~q9 + city_mapped, design = design)

# Ensure that 'q9' is a factor with levels ordered from 1 to 5
q9_levels_ordered <- as.factor(as.data.frame(q9_cross_tab_city)$q9)
q9_levels_ordered <- factor(q9_levels_ordered, levels = c("1", "2", "3", "4", "5"))

# Update the cross-tabulation data frame to use the ordered levels
q9_cross_tab_city_df <- as.data.frame(q9_cross_tab_city)
q9_cross_tab_city_df$q9 <- factor(q9_cross_tab_city_df$q9, levels = c("1", "2", "3", "4", "5"))

# Generate the plot
q9_pre_cross_tab_city_plot <- ggplot(q9_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q9)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) +
  labs(title = "Q12. Testing ideas or prototypes with community members.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

# Print the plot to display it in the document
print(q9_pre_cross_tab_city_plot)

Code

# Use ggsave to save the plot
ggsave("plots/q9_pre_cross_tab_city_plot.png", q9_pre_cross_tab_city_plot)

Q13. Creating wireframes or prototypes of digital products

Code

# Single-variable frequency plot
q10_frequency <- svytable(~q10, design = design)

q10_pre_frequency_plot <- ggplot(as.data.frame(q10_frequency), aes(x = q10, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q13. Creating wireframes or prototypes of digital products.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q10_pre_frequency_plot)

Code

ggsave("plots/q10_pre_frequency_plot.png", q10_pre_frequency_plot)


# Cross-tabulation with cities
q10_cross_tab_city <- svytable(~q10 + city_mapped, design = design)

q10_levels_ordered <- as.factor(as.data.frame(q10_cross_tab_city)$q10)
q10_levels_ordered <- factor(q10_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q10_cross_tab_city_df <- as.data.frame(q10_cross_tab_city)
q10_cross_tab_city_df$q10 <- factor(q10_cross_tab_city_df$q10, levels = c("1", "2", "3", "4", "5"))

q10_pre_cross_tab_city_plot <- ggplot(q10_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q10)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F")) +
  labs(title = "Q13. Creating wireframes or prototypes of digital products.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q10_pre_cross_tab_city_plot)

Code

ggsave("plots/q10_pre_cross_tab_city_plot.png", q10_pre_cross_tab_city_plot)

Q14. Synthesizing community research or feedback into insights or recommendations

Code

# Single-variable frequency plot
q11_frequency <- svytable(~q11, design = design)

q11_pre_frequency_plot <- ggplot(as.data.frame(q11_frequency), aes(x = q11, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Majority of respondents believe they moderately or experienced in their 
skills synthesizing community feedback into insights or recommendations.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

# Print the plot to display it in the document
print(q11_pre_frequency_plot)

Code

# Use ggsave to save the plot
ggsave("plots/q11_pre_frequency_plot.png", q11_pre_frequency_plot)


# Cross-tabulation with cities
q11_cross_tab_city <- svytable(~q11 + city_mapped, design = design)

q11_levels_ordered <- as.factor(as.data.frame(q11_cross_tab_city)$q11)
q11_levels_ordered <- factor(q11_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q11_cross_tab_city_df <- as.data.frame(q11_cross_tab_city)
q11_cross_tab_city_df$q11 <- factor(q11_cross_tab_city_df$q11, levels = c("1", "2", "3", "4", "5"))

q11_pre_cross_tab_city_plot <- ggplot(q11_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q11)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F")) +
  labs(title = "The most experienced to this skill are city teams that have
previously participated in the TOPC program.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q11_pre_cross_tab_city_plot)

Code

ggsave("plots/q11_pre_cross_tab_city_plot.png", q11_pre_cross_tab_city_plot)

Product Stewardship

Please rate your familiarity with the following skills.

Q16. Evaluating ideas/solutions based on community need, feasibility, or viability

Code

# Single-variable frequency plot
q13_frequency <- svytable(~q13, design = design)

all_levels_df <- data.frame(q13 = factor(1:5, levels = 1:5), Freq = integer(5))

q13_frequency_df <- merge(all_levels_df, as.data.frame(q13_frequency), by = "q13", all.x = TRUE)
q13_frequency_df$Freq <- rowSums(q13_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q13_pre_frequency_plot <- ggplot(q13_frequency_df, aes(x = q13, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q16. Evaluating ideas/solutions based on community need, feasibility, 
or viability.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q13_pre_frequency_plot)

Code

ggsave("plots/q13_pre_frequency_plot.png", q13_pre_frequency_plot)

# Cross-tabulation with cities
q13_cross_tab_city <- svytable(~q13 + city_mapped, design = design)

q13_levels_ordered <- as.factor(as.data.frame(q13_cross_tab_city)$q13)
q13_levels_ordered <- factor(q13_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q13_cross_tab_city_df <- as.data.frame(q13_cross_tab_city)
q13_cross_tab_city_df$q13 <- factor(q13_cross_tab_city_df$q13, levels = c("1", "2", "3", "4", "5"))

q13_pre_cross_tab_city_plot <- ggplot(q13_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q13)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) +
  labs(title = "Q16. Evaluating ideas/solutions based on community need, feasibility, 
or viability.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q13_pre_cross_tab_city_plot)

Code

ggsave("plots/q13_pre_cross_tab_city_plot.png", q13_pre_cross_tab_city_plot)

Q17. Co-designing solutions alongside community members

Code

# Single-variable frequency plot
q14_frequency <- svytable(~q14, design = design)

all_levels_df <- data.frame(q14 = factor(1:5, levels = 1:5), Freq = integer(5))

q14_frequency_df <- merge(all_levels_df, as.data.frame(q14_frequency), by = "q14", all.x = TRUE)
q14_frequency_df$Freq <- rowSums(q14_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q14_pre_frequency_plot <- ggplot(q14_frequency_df, aes(x = q14, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q17. Co-designing solutions alongside community members.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q14_pre_frequency_plot)

Code

ggsave("plots/q14_pre_frequency_plot.png", q14_pre_frequency_plot)

# Cross-tabulation with cities
q14_cross_tab_city <- svytable(~q14 + city_mapped, design = design)

q14_levels_ordered <- as.factor(as.data.frame(q14_cross_tab_city)$q14)
q14_levels_ordered <- factor(q14_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q14_cross_tab_city_df <- as.data.frame(q14_cross_tab_city)
q14_cross_tab_city_df$q14 <- factor(q14_cross_tab_city_df$q14, levels = c("1", "2", "3", "4", "5"))

q14_pre_cross_tab_city_plot <- ggplot(q14_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q14)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) +
  labs(title = "Q17. Co-designing solutions alongside community members.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q14_pre_cross_tab_city_plot)

Code

ggsave("plots/q14_pre_cross_tab_city_plot.png", q14_pre_cross_tab_city_plot)

Q18. Translating an idea/solution into a list of digital product requirements for tech teams

Code

# Single-variable frequency plot
q15_frequency <- svytable(~q15, design = design)

all_levels_df <- data.frame(q15 = factor(1:5, levels = 1:5), Freq = integer(5))

q15_frequency_df <- merge(all_levels_df, as.data.frame(q15_frequency), by = "q15", all.x = TRUE)
q15_frequency_df$Freq <- rowSums(q15_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q15_pre_frequency_plot <- ggplot(q15_frequency_df, aes(x = q15, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q18. Translating an idea/solution into a list of digital product 
requirements for tech teams.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q15_pre_frequency_plot)

Code

ggsave("plots/q15_pre_frequency_plot.png", q15_pre_frequency_plot)

# Cross-tabulation with cities
q15_cross_tab_city <- svytable(~q15 + city_mapped, design = design)

q15_levels_ordered <- as.factor(as.data.frame(q15_cross_tab_city)$q15)
q15_levels_ordered <- factor(q15_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q15_cross_tab_city_df <- as.data.frame(q15_cross_tab_city)
q15_cross_tab_city_df$q15 <- factor(q15_cross_tab_city_df$q15, levels = c("1", "2", "3", "4", "5"))

q15_pre_cross_tab_city_plot <- ggplot(q15_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q15)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Q18. Translating an idea/solution into a list of digital product 
requirements for tech teams.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q15_pre_cross_tab_city_plot)

Code

ggsave("plots/q15_pre_cross_tab_city_plot.png", q15_pre_cross_tab_city_plot)

Q19. Working with data (analyzing, cleaning, evaluating, etc.)

Code

# Single-variable frequency plot
q16_frequency <- svytable(~q16, design = design)

all_levels_df <- data.frame(q16 = factor(1:5, levels = 1:5), Freq = integer(5))

q16_frequency_df <- merge(all_levels_df, as.data.frame(q16_frequency), by = "q16", all.x = TRUE)
q16_frequency_df$Freq <- rowSums(q16_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q16_pre_frequency_plot <- ggplot(q16_frequency_df, aes(x = q16, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Majority of respondents believe they moderately or are experienced
in their skills working with data from analysis to evaluation.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q16_pre_frequency_plot)

Code

ggsave("plots/q16_pre_frequency_plot.png", q16_pre_frequency_plot)

# Cross-tabulation with cities
q16_cross_tab_city <- svytable(~q16 + city_mapped, design = design)

q16_levels_ordered <- as.factor(as.data.frame(q16_cross_tab_city)$q16)
q16_levels_ordered <- factor(q16_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q16_cross_tab_city_df <- as.data.frame(q16_cross_tab_city)
q16_cross_tab_city_df$q16 <- factor(q16_cross_tab_city_df$q16, levels = c("1", "2", "3", "4", "5"))

q16_pre_cross_tab_city_plot <- ggplot(q16_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q16)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "All city teams show moderate to expertise in skills working with data 
with slight variation in Akron of few respondents new to the skill.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q16_pre_cross_tab_city_plot)

Code

ggsave("plots/q16_pre_cross_tab_city_plot.png", q16_pre_cross_tab_city_plot)

Q20. Publishing, sharing, or integrating data into strategy, projects, or tools

Code

# Single-variable frequency plot
q17_frequency <- svytable(~q17, design = design)

all_levels_df <- data.frame(q17 = factor(1:5, levels = 1:5), Freq = integer(5))

q17_frequency_df <- merge(all_levels_df, as.data.frame(q17_frequency), by = "q17", all.x = TRUE)
q17_frequency_df$Freq <- rowSums(q17_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q17_pre_frequency_plot <- ggplot(q17_frequency_df, aes(x = q17, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Majority of respondents believe they moderately or are experienced
in their skills working with data from analysis to evaluation.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q17_pre_frequency_plot)

Code

ggsave("plots/q17_pre_frequency_plot.png", q17_pre_frequency_plot)

# Cross-tabulation with cities
q17_cross_tab_city <- svytable(~q17 + city_mapped, design = design)

q17_levels_ordered <- as.factor(as.data.frame(q17_cross_tab_city)$q17)
q17_levels_ordered <- factor(q17_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q17_cross_tab_city_df <- as.data.frame(q17_cross_tab_city)
q17_cross_tab_city_df$q17 <- factor(q17_cross_tab_city_df$q17, levels = c("1", "2", "3", "4", "5"))

q17_pre_cross_tab_city_plot <- ggplot(q17_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q17)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Half of respondents are quite familiar with publishing data into 
projects and the rest are either new or moderately familiarity.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q17_pre_cross_tab_city_plot)

Code

ggsave("plots/q17_pre_cross_tab_city_plot.png", q17_pre_cross_tab_city_plot)

Q21. Managing a community-facing digital product

Code

# Single-variable frequency plot
q18_frequency <- svytable(~q18, design = design)

all_levels_df <- data.frame(q18 = factor(1:5, levels = 1:5), Freq = integer(5))

q18_frequency_df <- merge(all_levels_df, as.data.frame(q18_frequency), by = "q18", all.x = TRUE)
q18_frequency_df$Freq <- rowSums(q18_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q18_pre_frequency_plot <- ggplot(q18_frequency_df, aes(x = q18, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q21. Managing a community-facing digital product.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q18_pre_frequency_plot)

Code

ggsave("plots/q18_pre_frequency_plot.png", q18_pre_frequency_plot)

# Cross-tabulation with cities
q18_cross_tab_city <- svytable(~q18 + city_mapped, design = design)

q18_levels_ordered <- as.factor(as.data.frame(q18_cross_tab_city)$q18)
q18_levels_ordered <- factor(q18_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q18_cross_tab_city_df <- as.data.frame(q18_cross_tab_city)
q18_cross_tab_city_df$q18 <- factor(q18_cross_tab_city_df$q18, levels = c("1", "2", "3", "4", "5"))

q18_pre_cross_tab_city_plot <- ggplot(q18_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q18)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Q21. Managing a community-facing digital product.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q18_pre_cross_tab_city_plot)

Code

ggsave("plots/q18_pre_cross_tab_city_plot.png", q18_pre_cross_tab_city_plot)

Q22. Drafting a plan to ensure long-term product success (including identifying budget and resources, updating product based on feedback, etc)

Code

# Single-variable frequency plot
q19_frequency <- svytable(~q19, design = design)

all_levels_df <- data.frame(q19 = factor(1:5, levels = 1:5), Freq = integer(5))

q19_frequency_df <- merge(all_levels_df, as.data.frame(q19_frequency), by = "q19", all.x = TRUE)
q19_frequency_df$Freq <- rowSums(q19_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q19_pre_frequency_plot <- ggplot(q19_frequency_df, aes(x = q19, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q22. Drafting a plan to ensure long-term product success (including 
identifying budget and resources, updating product based on feedback, etc).",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q19_pre_frequency_plot)

Code

ggsave("plots/q19_pre_frequency_plot.png", q19_pre_frequency_plot)

# Cross-tabulation with cities
q19_cross_tab_city <- svytable(~q19 + city_mapped, design = design)

q19_levels_ordered <- as.factor(as.data.frame(q19_cross_tab_city)$q19)
q19_levels_ordered <- factor(q19_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q19_cross_tab_city_df <- as.data.frame(q19_cross_tab_city)
q19_cross_tab_city_df$q19 <- factor(q19_cross_tab_city_df$q19, levels = c("1", "2", "3", "4", "5"))

q19_pre_cross_tab_city_plot <- ggplot(q19_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q19)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Q22. Drafting a plan to ensure long-term product success (including 
identifying budget and resources, updating product based on
feedback, etc).",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q19_pre_cross_tab_city_plot)

Code

ggsave("plots/q19_pre_cross_tab_city_plot.png", q19_pre_cross_tab_city_plot)

Goal 3: Participant Recognition and Application of Methods

Partnerships

Please rate your familiarity with the following skills.

Q24. Collaborating with community organizations on projects or programs

Code

# Single-variable frequency plot
q21_frequency <- svytable(~q21, design = design)

all_levels_df <- data.frame(q21 = factor(1:5, levels = 1:5), Freq = integer(5))

q21_frequency_df <- merge(all_levels_df, as.data.frame(q21_frequency), by = "q21", all.x = TRUE)
q21_frequency_df$Freq <- rowSums(q21_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q21_pre_frequency_plot <- ggplot(q21_frequency_df, aes(x = q21, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q24. Collaborating with community organizations on projects 
or programs.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q21_pre_frequency_plot)

Code

ggsave("plots/q21_pre_frequency_plot.png", q21_pre_frequency_plot)

# Cross-tabulation with cities
q21_cross_tab_city <- svytable(~q21 + city_mapped, design = design)

q21_levels_ordered <- as.factor(as.data.frame(q21_cross_tab_city)$q21)
q21_levels_ordered <- factor(q21_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q21_cross_tab_city_df <- as.data.frame(q21_cross_tab_city)
q21_cross_tab_city_df$q21 <- factor(q21_cross_tab_city_df$q21, levels = c("1", "2", "3", "4", "5"))

q21_pre_cross_tab_city_plot <- ggplot(q21_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q21)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Q24. Collaborating with community organizations on projects 
or programs.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q21_pre_cross_tab_city_plot)

Code

ggsave("plots/q21_pre_cross_tab_city_plot.png", q21_pre_cross_tab_city_plot)

Q25. Collaborating with other city/county departments on projects or programs

Code

# Single-variable frequency plot
q22_frequency <- svytable(~q22, design = design)

all_levels_df <- data.frame(q22 = factor(1:5, levels = 1:5), Freq = integer(5))

q22_frequency_df <- merge(all_levels_df, as.data.frame(q22_frequency), by = "q22", all.x = TRUE)
q22_frequency_df$Freq <- rowSums(q22_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q22_pre_frequency_plot <- ggplot(q22_frequency_df, aes(x = q22, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q25. Collaborating with other city/county departments on 
projects or programs.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q22_pre_frequency_plot)

Code

ggsave("plots/q22_pre_frequency_plot.png", q22_pre_frequency_plot)

# Cross-tabulation with cities
q22_cross_tab_city <- svytable(~q22 + city_mapped, design = design)

q22_levels_ordered <- as.factor(as.data.frame(q22_cross_tab_city)$q22)
q22_levels_ordered <- factor(q22_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q22_cross_tab_city_df <- as.data.frame(q22_cross_tab_city)
q22_cross_tab_city_df$q22 <- factor(q22_cross_tab_city_df$q22, levels = c("1", "2", "3", "4", "5"))

q22_pre_cross_tab_city_plot <- ggplot(q22_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q22)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Q25. Collaborating with other city/county departments on 
projects or programs.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q22_pre_cross_tab_city_plot)

Code

ggsave("plots/q22_pre_cross_tab_city_plot.png", q22_pre_cross_tab_city_plot)

Q26. Collaborating with an external tech or IT vendor on projects or programs

Code

# Single-variable frequency plot
q23_frequency <- svytable(~q23, design = design)

all_levels_df <- data.frame(q23 = factor(1:5, levels = 1:5), Freq = integer(5))

q23_frequency_df <- merge(all_levels_df, as.data.frame(q23_frequency), by = "q23", all.x = TRUE)
q23_frequency_df$Freq <- rowSums(q23_frequency_df[, c("Freq.x", "Freq.y")], na.rm = TRUE)

q23_pre_frequency_plot <- ggplot(q23_frequency_df, aes(x = q23, y = Freq)) +
  geom_bar(stat = "identity", fill = "#041e42", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  labs(title = "Q26. Collaborating with an external tech or IT vendor on 
projects or programs.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() + 
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0))

print(q23_pre_frequency_plot)

Code

ggsave("plots/q23_pre_frequency_plot.png", q23_pre_frequency_plot)

# Cross-tabulation with cities
q23_cross_tab_city <- svytable(~q23 + city_mapped, design = design)

q23_levels_ordered <- as.factor(as.data.frame(q23_cross_tab_city)$q23)
q23_levels_ordered <- factor(q23_levels_ordered, levels = c("1", "2", "3", "4", "5"))

q23_cross_tab_city_df <- as.data.frame(q23_cross_tab_city)
q23_cross_tab_city_df$q23 <- factor(q23_cross_tab_city_df$q23, levels = c("1", "2", "3", "4", "5"))

q23_pre_cross_tab_city_plot <- ggplot(q23_cross_tab_city_df, aes(x = city_mapped, y = Freq, fill = q23)) +
  geom_bar(stat = "identity", position = "stack", width = 0.7) +
  coord_flip() +
  expand_limits(y = c(0, total_sample)) + 
  scale_fill_manual(values = c("5" = "#041e42", "4" = "#6eaddc", "3" = "#da291c", "2" = "#1B786E", "1" = "#701C7F"),
                    drop=FALSE) + 
  labs(title = "Q26. Collaborating with an external tech or IT vendor on 
projects or programs.",
       subtitle = paste("Rating Scale: 5 - Expert to 1 - New (n =", total_sample, ")"),
       x = "",
       y = "Number of Responses",
       caption = "Source: TOPC Cohort 3 Survey (2023)") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption = element_text(hjust = 0)) +
  guides(fill = guide_legend(reverse = TRUE, nrow = 1, byrow = TRUE))

print(q23_pre_cross_tab_city_plot)

Code

ggsave("plots/q23_pre_cross_tab_city_plot.png", q23_pre_cross_tab_city_plot)